Mul$agentSimulaon*on* StarBED*...Wireless* mesh ..... Pluggable NetEmulaon PC Sensor NodesEmulaon...
Transcript of Mul$agentSimulaon*on* StarBED*...Wireless* mesh ..... Pluggable NetEmulaon PC Sensor NodesEmulaon...
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Mul$agent Simula$on on StarBED Deploying Agent-‐based Traffic Simula$on on StarBED
Rafik HADFI <[email protected]> Nagoya Ins$tute of Technology, Japan
October 20th, 2015
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Summary 1. Mo$va$ons 2. Architecture 3. The “agent” paradigm 4. MAS on StarBED stack 5. Traffic simula$on
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Mo$va$ons
• Agent-‐based, Large-‐scale, Distributed Simula$on of Complex Systems
• Case: traffic simula$on • Simula$on of complex behaviors using an agent-‐based model.
These behaviors include: – Mobility – Driving – Traffic synchroniza$on – Sensor networks
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Mo$va$ons
• A generic framework for programmable agent-‐based components – Mul$layered, with different levels of abstrac$ons referring to different simula$on layers
– Reusable API for both distributed and non-‐distributed simula$ons
– Genera$on of realis$c simula$on data
• A testbed for general purpose Computa$onal Intelligence – coordina$on – collabora$on – automated nego$a$on – etc.
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Architecture
• Mul$layered architecture built on a map
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Geographical Layer OSM
Architecture
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Geographical Layer OSM
Architecture
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Architecture
Traffic Synch Layer Traffic light
Geographical Layer OSM
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Architecture
Traffic Synch Layer Traffic light
Sensor Layer Sensor network
Geographical Layer OSM
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Architecture
Traffic Synch Layer Traffic light
Sensor Layer Sensor network
Agent Layer Driver agent
Mobile app agent
Geographical Layer OSM
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Architecture
Traffic Synch Layer Traffic light
Sensor Layer Sensor network
Agent Layer Driver agent
Mobile app agent
Consensus Layer Rou$ng
Nego$a$on Coopera$on Collabora$on
Geographical Layer OSM
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• Reac$ve, autonomous, collabora$ve, and goal-‐oriented agents – autonomous: parallel and distributed deployment (StarBED)
• Microscopic (autonomous) and macroscopic (collec$ve) simula$on
• An agent capable of – Reproducing realis$c mobility – Learning of preferences, behaviors (peers modeling) – Elicita$on – Op$miza$on – etc.
The “agent” paradigm
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• An agent can operate in two different ways: – Autonomous Interface Agent (AIA) assis$ng & interac$ng with humans
– Autonomous Agent (AA) to simulate the human
The “agent” paradigm
Learning • Behavior • Preferences • Ac$ons Assis$ng
Reported ac$ons, choices, type, preferences
Behaving with a par$cular type, profile, preferences
AIA Real behavior
Simulated behavior
AA
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MAS on StarBED stack
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プロバイダ網
Wireless mesh . . . . . Pluggable
Net Emula+on
PC Sensor
Nodes Emula+on
Web Twider
. . . . . Pluggable Wired (Ether)
Wireless (LTE)
Physical Layers
Satellite com
Car
Wireless (Wi-‐Fi)
IP Phone Embedded Navi app
StarBED
Android
Mobile app
MAS on StarBED stack
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Disaster scenario generator
People behavior
Physical state
Failures, etc.
プロバイダ網
Wireless mesh . . . . . Pluggable
Net Emula+on
PC Sensor
Nodes Emula+on
Web Twider
. . . . . Pluggable Wired (Ether)
Wireless (LTE)
Physical Layers
Physical Simula$on (Terrain data)
Determina+on of the affected areas
Satellite com
Car
Wireless (Wi-‐Fi)
IP Phone Embedded Navi app
StarBED
Android
Mobile app
scenario
MAS on StarBED stack
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Disaster scenario generator
People behavior
Physical state
Failures, etc.
プロバイダ網
Wireless mesh . . . . . Pluggable
Net Emula+on
PC Sensor
Nodes Emula+on
Web Twider
. . . . . Pluggable Wired (Ether)
Wireless (LTE)
Physical Layers
Physical Simula$on (Terrain data)
Determina+on of the affected areas
Agent θ3 . . . . . Pluggable
Agent
Rou$ng . . . . . Pluggable
MAS
Mechanism Design Conges$on
Satellite com
Car
Wireless (Wi-‐Fi)
Agent θ2
Coordina$on
IP Phone Embedded Navi app
StarBED
Behavioral & Economic type
Macroscopic & Organiza+onal type
§ Strategic types? § Risk aversion? § Uncertainty aversion? § Preferences/U$lity? § Coopera$ve behavior?
Encounter Protocols
Agent θ1
Android
Mobile app
scenario
MAS on StarBED stack
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Disaster scenario generator
People behavior
Physical state
Failures, etc.
プロバイダ網
Wireless mesh . . . . . Pluggable
Net Emula+on
PC Sensor
Nodes Emula+on
Web Twider
. . . . . Pluggable Wired (Ether)
Wireless (LTE)
Physical Layers
Physical Simula$on (Terrain data)
Determina+on of the affected areas
Agent θ3 . . . . . Pluggable
Agent
Rou$ng . . . . . Pluggable
MAS
Mechanism Design Conges$on
Satellite com
Car
Wireless (Wi-‐Fi)
Agent θ2
Coordina$on
IP Phone Embedded Navi app
StarBED
Behavioral & Economic type
Macroscopic & Organiza+onal type
§ Strategic types? § Risk aversion? § Uncertainty aversion? § Preferences/U$lity? § Coopera$ve behavior?
Encounter Protocols
Agent θ1
Android
Mobile app
scenario
MAS on StarBED stack
Behavioral models β1 ...βn emula$ng the mobility of the drivers, or cars, which is different from an AIA assis$ng the drivers
Example: Emula$ng a traffic coordina$on task in a disaster scenario (congested) involving car-‐embedded & mobile app agents represen$ng risk seeking, uncertainty averse drivers (only α% are coopera$ve). The coordina$on mechanism uses a sensor network
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Experiment Network
Boo$ng instruc$ons Configura$on data Agent stuff, media$on
Agent Agent
(x, y) at t
Visualizer
Qomet Qomet
Experiment Node Experiment Node
(x, y) at t Agent stuff
Simula$on Manager or
MAS on StarBED stack
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Experiment Network
Boo$ng instruc$ons Configura$on data Agent stuff, media$on
Agent Agent
(x, y) at t
Visualizer
Qomet Qomet
Experiment Node Experiment Node
(x, y) at t Agent stuff
Simula$on Manager or
MAS on StarBED stack
Agent
OSM DM
MAP
BM Visualizer
Server
OSM DM
MAP
Server
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Traffic simula$on
• Agents will have to emulate drivers behavioral models
• Dynamic and interac$ve mobility genera$on based on 3 forces – Speed v – Accelera$on a – Breaking b
• Interac$on with external forces – Driver ac$ons, through the steering wheel – Environment: traffic lights, routes – Interac$on with other vehicles
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Traffic simula$on
Speed difference: Example of rules:
δv!< −3
a!= 0
b!= δv!*urgency
δv!> 3
a!= δv!*urgency
b!= 0
objv!= 0
a!= 0
b!= −v!
δv!= objv
!− v!
Speed modula$on
Default speed, or “desired” speed
v!
a!
b!
objv!
Current speed, resul$ng from the interac$on with the environment
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Traffic simula$on
Speed difference: Example of rules:
δv!< −3
a!= 0
b!= δv!*urgency
δv!> 3
a!= δv!*urgency
b!= 0
objv!= 0
a!= 0
b!= −v!
δv!= objv
!− v!
Speed modula$on
Default speed, or “desired” speed
v!
a!
b!
objv!
is based on the steering wheel posi$on and how it changes given any change in the overall direc$on of the vehicle and its velocity angle (θ) Update rule
Angle modula$on
Current speed, resul$ng from the interac$on with the environment
δθ = objd!"−θ
Vsw =Vswt −Vswt−1
Vsw =Vswt−1 +δθ
Default direc$on
Steering wheel value at t-‐1
-‐1 1
Vsw
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• Rule-‐based direc$ves or programmable goal-‐oriented agents
Traffic simula$on
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Traffic simula$on Agent-‐based, microscopic prototype (before distribu$on)
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Traffic simula$on Agent-‐based, distributed, macroscopic prototype
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Thank you